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Preferential Solvation of the Antioxidant Agent Daidzein in some Aqueous Co-Solvent Mixtures according to IKBI and QLQC Methods

2015· article· en· W1905852396 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Applied Solution Chemistry and Modeling · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsSolvationChemistrySolventAqueous solutionSolvation shellSolvent effectsComputational chemistryOrganic chemistryPhysical chemistryInorganic chemistry

Abstract

fetched live from OpenAlex

The preferential solvation parameters by ethanol (EtOH) or propylene glycol (PG) of daidzein were derived from their solution thermodynamic properties by means of the inverse Kirkwood-Buff integrals and the quasi-lattice-quasi-chemical (QLQC) methods. According to IKBI method, the preferential solvation parameter by the co-solvent, δx1,3, is negative in water-rich mixtures but positive in co-solvent-rich mixtures in both kinds of systems. This could demonstrate the relevant role of hydrophobic hydration around the aromatic rings in the drug solvation in water-rich mixtures. Furthermore, the more solvation by co-solvent in co-solvent-rich mixtures could be due mainly to polarity effects and acidic behavior of the hydroxyl groups of the compound in front to the more basic solvents present in the mixtures, i.e. EtOH or PG. Otherwise, according to QLQC method, this drug is preferentially solvated by the co-solvents in all the mixtures in both kind of systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.312
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it